We consider the problem of fitting a large-scale covariance matrix to multivariate Gaussian data in such a way that the inverse is sparse, thus providing model selection. Beginnin...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
We extend the well-known BFGS quasi-Newton method and its memory-limited variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by ge...
We introduce a linearly weighted variant of the total
variation for vector fields in order to formulate regularizers
for multi-class labeling problems with non-trivial interclass...
Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative t...
We present a fully dynamic randomized data structure that can answer queries about the convex hull of a set of n points in three dimensions, where insertions take O(log3 n) expect...